Hierarchical Model Predictive Control Research Articles

Integrated battery thermal and energy management for electric vehicles with hybrid energy storage system: A hierarchical approach

Battery cooling is crucial for electric vehicles’ thermal safety, energy consumption, and battery life in hot climatic conditions. For electric vehicles with battery/supercapacitor hybrid energy storage system, battery cooling is deeply coupled with load power split from the electrical-thermal-aging perspective, leading to challenging thermal and energy management issues. This paper proposes a hierarchical multi-horizon model predictive control (MH-MPC) method to optimize battery cooling and energy management simultaneously. First, the electrical-thermal-aging coupling relationship between battery cooling and energy management is systematically analyzed. Then, by decoupling a centralized MH-MPC, an upper-level MH-MPC is designed to optimize the battery capacity loss cost and battery cooling cost by generating optimal compressor power, then a lower-level MH-MPC tends to minimize the battery capacity loss cost by allocating the total load power demand. The prediction horizon and sampling time are determined. Numerical results show that, compared with the centralized method, the proposed hierarchical method provides a lower battery capacity loss for long-term driving with only about 20% computation burden. Compared with standalone energy management without battery cooling, the total cost can be reduced by 12%–16% under long-term driving. Compared with optimizing energy management with Bangbang cooling, the battery degradation and total costs can be reduced by 15%–52% under short-term driving without deteriorating long-term performance.

Optimal IOFL-based economic model predictive control technique for boiler-turbine system

The optimal control design of the boiler-turbine system is vital to ensure feasibility and high responsiveness over desired load variations. Using the traditional linear control techniques realization of this task is difficult, as the boiler-turbine mechanism has strong nonlinearities. Besides, environmental and economic concerns have replaced existing tracking control ones as the primary concerns of advanced power plants. Thus, this study proposes an optimal economic model predictive controller (EMPC) scheme for this unit on the basis of the input/output feedback linearization (IOFL) method. By employing the IOFL method, this unit is decoupled into a new linearized model that is utilized for developing the suggested optimal IOFL EMPC technique. The proposed control scheme is formulated in an economic quadratic programming form that considers the input-rate and input limits of the unit for optimal economic performance. In addition, an adaptive iterative algorithm is utilized for constraints mapping with guaranteeing a feasible solution in a finite number of steps without violation of original constraints over the entire predictive horizon. The outcomes of the simulation show that the suggested optimal IOFL EMPC scheme offers an improved dynamic and economic output performance over fuzzy hierarchical MPC, fuzzy EMPC, and nonlinear EMPC techniques during various load variations.

Observer-based hierarchical distributed model predictive control for multi-linear motor traction systems

This paper proposes an observer-based hierarchical distributed model predictive control (MPC) strategy for ensuring speed consistency in multi-linear motor traction systems. First, a communication topology is considered to ensure information exchange. Secondly, the control architecture of each agent is divided into upper layers and lower layers. The upper layer utilizes a distributed MPC method to track the leader’s speed. The lower layer uses a decentralized MPC method to track the command signals sent by its upper layer controller. In addition, to eliminate the negative impact of disturbance, a nonlinear disturbance observer is designed. We then prove the asymptotic stability of the entire system by properly designing the Lyapunov equation. Finally, the feasibility of the proposed strategy is verified based on several simulations.

Hierarchical-stochastic model predictive control for a grid-interactive multi-zone residential building with distributed energy resources

Using Model Predictive Control (MPC) is a promising method for enabling grid-interactive efficient buildings. Since MPC relies on a building model and the forecasts of external disturbances to derive optimal inputs, the uncertainties due to forecast errors and model inaccuracies can deteriorate the control performance. Most existing MPC studies for the built environment are deterministic MPC without consideration of uncertainties even though several different methods (e.g., stochastic MPC) are available in the literature to deal with them. Even studies that consider forecast uncertainties often neglect model inaccuracies. Hence, in the present paper, a novel hierarchical-stochastic MPC is proposed considering forecast uncertainties and model inaccuracies, and its performance is compared with deterministic, stochastic, and hierarchical MPC for power management in a residential building with distributed energy resources. The control objective is the cost-optimal scheduling of a heat pump, a battery for energy storage, and a rooftop photovoltaic system. Measurement data is used to identify the building model. The results for one-week simulation in winter show that (1) the deterministic MPC results in an unacceptable level of temperature constraint violations in two out of five rooms; (2) the hierarchical MPC can reduce the temperature constraint violations to an acceptable level at the expense of increased cost; (3) the stochastic MPC achieves the same reduction in temperature constraint violations as the hierarchical MPC but at slightly lower costs; and (4) the new proposed hierarchical-stochastic MPC results in both lower temperature constraint violations and lower financial expenses than the use of stochastic or hierarchical MPC individually.

Data-driven Koopman model predictive control for hybrid energy storage system of electric vehicles under vehicle-following scenarios

In the current studies on energy management strategy (EMS) for vehicle-following scenarios, the accuracy of vehicle state predictions based on mechanistic models is influenced by the time-varying conditions, affecting the optimization control performance. To address this issue, a data-driven Koopman model predictive control for hybrid energy storage system (HESS) of electric vehicles (EVs) in vehicle-following scenarios is proposed, combining the safety speed planning and energy management strategy. Firstly, a data-driven Koopman vehicle state prediction model is constructed in the upper layer for estimating parameters, such as road surface smoothness and slope. This model is then integrated into Model Predictive Control (MPC) to optimize the speed of the following vehicle. Subsequently, in the lower layer, utilizing the output from the upper layer and predicting load power, the load power is further allocated within the HESS. Simulation results demonstrate that in scenarios considering factors like slope, the hierarchical MPC with the Koopman model reduces energy consumption by 5.55% compared to the hierarchical MPC with mechanistic model.

Real-time three-level energy management strategy for series hybrid wheel loaders based on WG-MPC

The application of Hybrid Electric Wheel Loaders (HEWL) represents an attractive option for future industrial development. In order to reduce the equivalent fuel consumption and extend the lifetime of the battery, a real time whale optimal algorithm and general regression neural network (WOA-GRNN) based hierarchical model predictive control (WG-MPC) energy management strategy for HEWL is proposed. The structure of the WG-MPC consists of three layers, namely, the prediction layer, the economic layer and the control layer. In the prediction layer, a prediction model based on WOA-GRNN is proposed for the complex demand power prediction. In the economic layer, the equivalent diesel cost, diesel generator operation cost, the lifetime cost of the diesel generator and the battery pack are considered. The control layer is designed based on improved MPC. Through the simulation analysis, the proposed WOA-GRNN prediction model achieves the best prediction accuracy compared with the traditional prediction methods. Compared with FT-MPC and ECMS, the total economic cost of WG-MPC is reduced by 3.42% and 5.12%, respectively. The simulation results demonstrate that the proposed WG-MPC strategy has good control performance, high computational efficiency, low equivalent diesel consumption, longer battery lifetime and good use cost economy, which is of great significance for practical applications.

Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks

Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks

Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric Vehicles

AbstractBattery management systems are important for the safe and efficient operation of electric vehicles. Although high hardware performance and effective configurations of batteries have been realized, a management algorithm is required for ensuring optimal system performance. This review focuses on optimal controllers for charging, thermal control, and cell balancing of electric vehicles. A potential approach for practical applications is the direct optimal control method, particularly model predictive control (MPC). The objective function, prediction model types, and manipulated variables are summarized, along with the computational performance. Typical nonlinear MPC, linear MPC, explicit MPC, and hierarchical MPC are the main formulations for the optimal control of EVs. The AI‐based approach learns the optimal control law as a function from the optimal control result data. Although few studies have applied the reinforcement approach to battery systems, additional safety considerations for constraints must be considered for real applications. Cell variations, aging factors, and uncertainty considerations have been analyzed for improving the controller design. Addressing the computational issue with a reliable optimizer is critical to the implementation of an optimal controller for EVs.

Fuel and Emissions Optimization for Connected Diesel Engine Vehicles With Hierarchical Model Predictive Control

Fuel and Emissions Optimization for Connected Diesel Engine Vehicles With Hierarchical Model Predictive Control

Co-optimization of Energy Management and Eco-Driving Considering Fuel Cell Degradation Via Improved Hierarchical Model Predictive Control

An advanced eco-driving technology is widely recognized as having enormous potential to reduce the vehicle fuel consumption. However, most research on eco-driving focuses on the stability and safety for vehicle operating while disregarding its comfort and economy. To meet the requirements for safety and comfort, at the same time, enhance the economic performance of the vehicles, an improved hierarchical model predictive control cooperative optimization strategy is proposed for fuel cell hybrid electric vehicle with car-following scenario. Specifically, the upper-level model predictive controller controls the velocity, inter-vehicle distance and acceleration to guarantee safety and comfort for driving. According to the velocity information obtained from the upper model predictive controller, the lower-level improved model predictive controller considers the impact of disturbance changes on vehicle economy and aims to minimize the vehicle operating cost considering fuel cell degradation, so as to allocate energy rationally. Finally, the enhancement of economic performance of proposed strategy is verified with the results of comparative study that 3.09% economic improvement on the premise of assuring safety and comfort of driving.

A Hierarchical MPC Approach for Arriving-Phase Operation of Virtually Coupled Train Set

A Hierarchical MPC Approach for Arriving-Phase Operation of Virtually Coupled Train Set

Regional decentralized coordinated AGC based on hierarchical model predictive control

Regional decentralized coordinated AGC based on hierarchical model predictive control

Real-Time Hierarchical MPC Applied to an MMC in Grid-Forming Mode: Implementation and Validation in Power Hardware-in-The-Loop

Modular Multilevel Converters (MMC) are now expected to fulfill AC and DC grid stability functions while at the same time they must ensure their own operation. In this paper, we propose a new MIMO control structure to coordinate the AC/DC power exchange and MMC internal energy control using a hierarchical MPC. This controller is tested on an AC/DC system where the MMC works in grid-forming mode on the AC side and controls the DC-grid voltage at the same time. This MPC solution is implemented in a real-time controller to operate a physical mockup of a 6kVA MMC which contains 60 submodules and interfaces a 400V DC grid and a 120V (line-to-ground) AC grid. The MPC solution shows similar tracking performance with the state-of-the-art dual PI control solution based on two PI controllers working in parallel, and exhibits a better disturbance rejection capacity than this dual PI.

Hierarchical model predictive control for islanded and grid-connected microgrids with wind generation and hydrogen energy storage systems

This paper presents a novel energy management strategy (EMS) to control a wind-hydrogen microgrid which includes a wind turbine paired with a hydrogen-based energy storage system (HESS), i.e., hydrogen production, storage and re-electrification facilities, and a local load. This complies with the mini-grid use case as per the IEA-HIA Task 24 Final Report, where three different use cases and configurations of wind farms paired with HESS are proposed in order to promote the integration of wind energy into the grid. Hydrogen production surpluses by wind generation are stored and used to provide a demand-side management solution for energy supply to the local and contractual loads, both in the grid-islanded and connected modes, with corresponding different control objectives. The EMS is based on a hierarchical model predictive control (MPC) in which long-term and short-term operations are addressed. The long-term operations are managed by a high-level MPC, in which power production by wind generation and load demand forecasts are considered in combination with day-ahead market participation. Accordingly, the hydrogen production and re-electrification are scheduled so as to jointly track the load demand, maximize the revenue through electricity market participation and minimize the HESS operating costs. Instead, the management of the short-term operations is entrusted to a low-level MPC, which compensates for any deviations of the actual conditions from the forecasts and refines the power production so as to address the real-time market participation and the short time-scale equipment dynamics and constraints. Both levels also take into account operation requirements and devices’ operating ranges through appropriate constraints. The mathematical modeling relies on the mixed-logic dynamic (MLD) framework so that the various logic states and corresponding continuous dynamics of the HESS are considered. This results in a mixed-integer linear program which is solved numerically. The effectiveness of the controller is analyzed by simulations which are carried out using wind forecasts and spot prices of a wind farm in center-south of Italy.

Hierarchical MPC for a dynamic process system employing parametric global optimization strategy

The hierarchical decision making in process industries has been traditionally viewed as having a common objective, such as the overall cost, which needs to be optimized. However, a more appropriate approach is to formulate and solve hierarchical optimization and control problems. The solution algorithms for hierarchical optimization problems have been reported in the literature. The idea is to recast each optimization sub-problem in the hierarchy into a multiparametric programming problem, considering the variables of upper-level problems as unknown parameters. In this paper, explicit Model Predictive Control (MPC) and hierarchical optimization techniques, employing multiparametric programming, are combined for hierarchical MPC. The solution algorithm for hierarchical MPC is described in detail. Note that the solution to a hierarchical MPC problem is challenging, even for the simplest case of linear-quadratic objectives. Closed-loop simulations of a thermal mixing process, under two different hierarchical MPC formulations, are performed and the control performance is studied.

Energy Balance in a Standalone PV Battery Hybrid Generation System on Solar-Powered Aircraft Using the Model Predictive Control Method

This paper proposes a battery state of charge (SOC)-based energy management strategy using hierarchical distributed model predictive control (HDMPC) for a standalone microgrid on solar-powered long-endurance aircraft. The microgrid was innovatively designed as a two-layer structure in which the first layer consists of a photovoltaic generation and battery storage system named the PV battery module (PBM). The second layer, named the microgrid subsystem (MGSS), consists of several PBMs, each of which supplies power to a specific DC load on the aircraft. The control system is divided into two levels: the grid-level model predictive control (MPC) and the converter-level MPC. The grid-level MPC adopts a distributed model predictive control strategy to obtain the reference power of each module. The converter-level MPC calculates the control variables of converters using a supervisory model predictive control (SMPC) strategy. The new microgrid structure and the proposed control strategy have improved the reliability of the energy system and increased its energy utilization rate.

Hierarchical Model-Predictive-Control-Based Energy Management Strategy for Fuel Cell Hybrid Commercial Vehicles Incorporating Traffic Information

With the development of intelligent transportation systems, access to diverse transportation information has become possible. Integrating this information into an energy management strategy will make the energy allocation prospective and thus improve the overall performance of the energy management program. For this reason, this paper proposes a hierarchical model predictive control (MPC) energy management strategy that incorporates traffic information, where the upper layer plans the vehicle’s velocity based on the traffic information and the lower layer optimizes the energy distribution of the vehicle based on the planned velocity. In order to improve the accuracy of the planning speed of the upper strategy, a dung beetle optimization-radial basis function (DBO-RBF) prediction model is constructed, artfully optimizing the RBF neural network using the dung beetle optimization algorithm. The results show that the prediction accuracy is improved by 13.96% at a prediction length of 5 s. Further, when the vehicle passes through a traffic light intersection, the traffic light information is also considered in the upper strategy to plan a more economical speed and improve the traffic efficiency of the vehicle and traffic utilization. Finally, a dynamic programming (DP)-based solver is designed in the lower layer of the strategy, which optimizes the energy distribution of the vehicle according to the velocity planned by the upper layer to improve the economy of the vehicle. The results demonstrate achieving a noteworthy 3.97% improvement in fuel economy compared to the conventional rule-based energy management strategy and allowing drivers to proceed through red light intersections without stopping. This proves a substantial performance enhancement in energy management strategies resulting from the integration of transportation information.

Hierarchical multi-time-scale predictive thermal management and fuel optimization for heavy-duty compression ignition engines

For heavy-duty diesel engines, NOX emissions reduction is strongly constrained by fuel efficiency. This paper presents a hierarchical model predictive controller (H-MPC) for coordinated control of tailpipe NOX emissions and fuel consumption. The H-MPC uses the separation of slow and fast dynamics that exist in the engine and its aftertreatment system. The controller is synthesized with an architecture in which a high-level MPC uses a longer prediction horizon compared to the low-level predictive controller which tracks the high-level controller command and manages the thermal dynamics of the aftertreatment system. Engine load preview enables the high-level controller to estimate the desired catalyst temperature ahead of time and addresses the selective catalytic reduction (SCR) slow thermal dynamics. Calculated by the high-level controller, the intake manifold pressure, and the start of injection (SOI) crank angle is used as reference trajectories in the low-level controller that regulates fast dynamical behaviors such as engine out NOX emissions. Hardware-in-the-loop (HIL) validation of this integrated H-MPC on a rapid prototype controller shows that when the SCR catalyst temperature is above light-off temperature (warmed-up condition), the engine operation is shifted to operate with the best fuel economy since the warmed-up SCR can efficiently reduce the engine-out NOX emissions. Results indicate that up to 0.8% benefit in cycle averaged BSFC along with a 13% reduction in tailpipe NOX compared to a stock engine calibration can be achieved with the coordinated engine and aftertreatment system through H-MPC.

Tire-Road Peak Adhesion Coefficient Estimation Based on Multisource Information Assessment

The tire-road peak adhesion coefficient (TRPAC) is defined as the ratio of the peak adhesion to the vertical load of the tire, which can characterize the ability of a tire to adhere to the road. Reliable TRPAC estimation can not only benefit the vehicle active safety system, but also serve the intelligent transportation system to improve the safety of traffic participants. Considering the problems of low estimation accuracy and poor real-time performance caused by low-quality sensor information in existing TRPAC estimation methods, a TRPAC fusion estimation framework based on the assessment of multisource information quality is proposed in this paper. Based on the observability theory of nonlinear systems, a quantitative indicator of dynamic information quality denoted as the excitation level is established. The region of effective excitation is defined as the criterion for starting and stopping the proposed dynamics-based fusion estimator for the longitudinal-lateral coupling condition. Considering occupant comfort and vehicle stability, an active enhancement method based on hierarchical model predictive control is designed to actively improve the excitation level. Based on the receding horizon optimization theory, a dynamics-image-based fusion estimator is proposed to make full use of visual and dynamic information. An adaptive fusion estimation strategy is then proposed to apply the proper estimation working mode according to the multisource information quality. The results of the simulation and vehicle test show that the proposed framework can still perform competitively when the quality of multisource information is poor and achieve reliable TRPAC estimation.

A safe and efficient decision algorithm for ramp merging based on a hierarchical planner and model predictive control

Due to highly dynamic interactions between vehicles, ramp merging decision is extraordinarily challenging in dense traffic. To deal with the non-convexity and inter-coupling of the decision-making problem, a top-level behavioural planner is proposed, which considers both the overall traffic situation and the individual characteristics of other interacting drivers. To ensure the implementability and efficiency of planning, a bottom-level motion planner is further designed with the guide of the top-level behavioural planner. The validation simulation using a naturalistic driving dataset shows that the proposed planning algorithm can achieve a success rate of 97.65% and has similar gap selection decision as human drivers. Then to track the planned vehicle motion, a nonlinear model predictive controller considering actuator delay and lag characteristics is proposed. Finally, the proposed planning and control modules are deployed in a turbocharged test vehicle, with satisfactory vehicle lateral and speed tracking errors, which validate the implementability of the proposed ramp merging decision algorithms.